Difference between revisions of "Applied Computer Vision Lecture Schedule"

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Revision as of 19:47, 20 August 2017

|CARNEGIE MELLON UNIVERSITY AFRICA|

Date Lecture Topic Material covered Reading Assignments
Mon. 28 Aug. 1 Overview Human and computer vision Lecture 1 Slides. Szeliski 2010, Sections 1.1 and 1.2. Kragic and Vincze, 2010.
Wed. 30 Aug. 2 Software tools OpenCV, Software development tools for course work Lecture 2 Slides.
Mon. 4 Sept. 3 Optics, sensors, and image formation Illumination, projections, lenses, Gauss lens equation, field of view, depth of field, CMOS and CCD sensors, colour sensors, noise, resolution.
Wed. 6 Sept. 4 Image acquisition and image representation Sampling and quantization, Shannon's sampling theorem, Nyquist frequency, Nyquist sampling rate, aliasing, resolution, space-variant sampling, log-polar images, dynamic range, colour spaces: HIS, HLS, HSV
Mon. 11 Sept. 5 Image processing Point & neighbourhood operations, image filtering, convolution, Fourier transform
Wed. 13 Sept. 6 Image processing Morphological operations
Mon. 18 Sept. 7 Image processing Geometric operations
Wed. 20 Sept. 8 Segmentation Region-based approaches, binary thresholding, connected component analysis
Mon. 25 Sept. 9 Segmentation Edge detection
Wed. 27 Feb. 10 Segmentation Colour-based approaches; k-means clustering
Mon. 2 Oct. 11 Image features Harris and Difference of Gaussian interest point operators
Wed. 4 Oct. 12 Image features SIFT feature descriptor
Mon. 9 Oct. 13 Object recognition Template matching; normalized cross-correlation; chamfer matching
Wed. 11 Oct. 14 Object recognition 2D shape features; statistical pattern recognition
Mon. 16 Oct. 15 Object recognition Hough transform for parametric curves: lines, circles, and ellipses
Wed. 18 Oct. 16 Object recognition Generalized Hough transform; extension to codeword features
Mon. 23 Oct. 17 Object recognition Colour histogram matching and back-projection
Wed. 25 Oct. 18 Object recognition Haar features and boosted classifiers
Mon. 30 Oct. 19 Object recognition Histogram of Oriented Gradients (HOG) feature descriptor
Wed. 1 Nov. 20 Video image processing Background subtraction and object tracking
Mon. 6 Nov. 21 3D vision Homogeneous transformations. Perspective transformation. Camera model and inverse perspective transformation
Wed. 8 Nov. 22 Stereo vision. Stereo correspondence, Epipolar geometry
Mon. 13 Nov. 23 Optical flow
Wed. 15 Nov. 24 Visual attention Saliency, Bottom-up and top-down attention
Mon. 20 Nov. 25 Clustering, grouping, and segmentation Gestalt principles. Clustering algorithms
Wed. 22 Nov. 26 Object recognition in 3D Object detection, object recognition, object categorisation
Mon. 27 Nov. 27 Affordances
Wed. 29 Nov. 28 Computer vision and machine learning
Mon. 4 Dec. 29 Computer vision and machine learning
Wed. 6 Dec. 30 Computer vision and machine learning



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